Data Virtualization with a No-code Touch

The challenges of data management today are evident. The sheer amount of data needing storage, all the different data formats and the plethora of locations data is scattered over, the lack of data consistency… How to deal with those challenges is up for debate. The challenge becomes even more daunting when the data must be real-time. The conventional data architecture built around data warehouses can no longer deliver the results sought by data users.

The billion-dollar question is:

“Is data integration possible without a data warehouse?”

In other words, can we integrate our data in real-time without having to copy and move it or invest in expensive hardware? Proponents of the data virtualization concept think that it is possible.

What is data virtualization?

Data virtualization is a data integration technique that introduces a semantic layer on top of a host of distributed data sources. This semantic layer can be accessed via SQL, REST, or GraphQL. In a sense, data virtualization merges all the different data sources into a single database users can access in real-time. The advantages of data virtualization are manifold:

Pros of data virtualization

• Real-time data availability is the biggest appeal of data virtualization. Data virtualization is based on the “zero replication, zero relocation” principle: Data does not have to be copied, moved, or synced—all processes that take time. Data virtualization not only allows for access to real-time data but also minimizes the risk of data loss since data is not moved.

• Data virtualization helps eliminate data silos and presents a single source of truth for everyone in the organization to draw their data from. This single source of truth provides the decision-makers with a holistic view of their data and makes it easier to receive buy-in from other stakeholders.

• By not moving the data, data virtualization puts an end to the data sprawl problem. It stops data from continuously getting duplicated and modified in different locations without any syncing in between and thus prevents the degradation of data quality.

• Data virtualization also empowers knowledge workers to self-serve—something Zhamak Dehghani envisioned with her “data mesh” concept. Without an ETL process to curate data and prepare it for consumption, data consumers will no longer be reliant on an IT department overwhelmed by demand.

• Finally, data virtualization replacing a data warehouse-based architecture removes the need to build and maintain data warehouses. Thanks to the lack of physical data integration in data virtualization, organizations do not have to invest in expensive hardware. Normally, adding new nodes to a data warehouse is a time-consuming process that takes a lot of work hours from highly-skilled personnel. With data virtualization, the process is straightforward, requiring no extra effort to reconfigure the system.

Cons of data virtualization

• While enjoying all these benefits brought to you by data virtualization, you are sacrificing data historicity. The data that this technique brings together and displays is just a snapshot. The lack of historicity prevents you from tracing the data back to its earlier version and tracking its change over time. Combining data with a data warehouse would overcome this hurdle if data historicity is indispensable to your organization.

• Servicing a host of improvised data analytics requests from different users can put stress on the source systems, raising scalability issues. Caching can help compensate for the dip in performance in such cases, but it comes at the expense of a slight increase in latency.

• Downtimes of the source systems may render data virtualization unusable. Therefore, a holistic approach to maximize source system uptime is required.

Data virtualization and Peaka

Every organization, regardless of its size, needs to harness the power of data one way or another. You can be the founder, CEO, and product development team of your startup all at the same time, and you still must gather data to inform your decision-making. Current solutions in the market are optimized for the enterprise segment: They are good at managing a sophisticated data infrastructure and integrating massive amounts of data. They come at a considerable price, though. Therefore, these solutions are a bit of an overkill for the needs of startups and SMBs, which neither require such sophisticated capabilities nor possess the means to pay for them or maintain them.

In today’s hyper-connected world, data gets created in multiple locations, flows from one platform to another, and blends into other data. We at Peaka recognize the need for startups and SMBs to bring together data from various SaaS tools, whether it is Stripe, Zendesk, Twilio, Shopify, or Hubspot. The new version of Peaka allows you to access real-time data and consolidate it without copying or moving it. You can then query your data and execute the analytics tasks as you see fit.

The dreaded scalability issue is no longer a problem with Peaka. The scalability problems usually arise from the limited capacity of the APIs to service queries. When you send a query with Peaka, the platform caches your data for a few minutes just in case you may need it for further queries. The later queries you send during that time frame get answered from the cached data without putting any stress on the APIs.

In addition to the slick data integration, Peaka’s new version also allows you to automate tasks that you otherwise have to do manually every day. You can even create triggers for the scheduled batch jobs and access real-time data without paying for extra features that you will never use. For example, would you like to nudge the users on your e-commerce platform when they have items remaining in their shopping carts at the end of the day? What if you want to send an email to every user who quits the onboarding walkthrough on your app? Peaka takes care of such tasks with ease.

The true distinguishing aspect of Peaka lies in how it blends data integrations with app building. Our platform lets you build responsive web apps or internal tools on top of the data you brought together from different sources. Using data to improve decision-making makes you more efficient at work. Using it to create apps that can get things done has the potential to transform your whole business.

Peaka shows you the no-code way of harnessing the power of your data. Whether you will use it to generate reports, metrics, or tools that will give you a leg over your competition is up to you.